Gene signature for diagnosis and prognosis of breast cancer and ovarian cancer

ABSTRACT

A first embodiment is a breast cancer prognosticator comprising a detection mechanism consisting a 15-gene signature. In addition there are embodiments comprised of 23-gene signatures and 28-gene signatures. The 28-gene signature may also be used for the prognosis of ovarian cancer. A second embodiment is a method to determine metastatic potential, relapse potential, or both in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and either the 15, 23, or 28-gene signatures, and developing a quantitative expression profile. Utilizing risk analysis the individual can be placed into one of two or more groups predicting risk and/or clincopathogic variables. Another embodiment is a method to determine relapse free potential in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and a 24-gene signature, and developing a quantitative expression profile.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of United States patent application 12/077,992 filed on Mar. 24, 2008.

REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTING COMPACT DISC APPENDIX

This application contains a Sequence Listing submitted on compact disk containing file name 387.Seq. The sequence listing on the compact disc is incorporated by reference herein in its entirety.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following figures are not drawn to scale and are for illustrative purposes only.

FIG. 1 is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts. FIG. 1 is a Time dependent ROC (t=5 years) curve of the 28-gene signature on the training set from Sotiriou et al. (8) The area under the ROC curve (AUC)=0.983.

FIG. 2 is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts. FIG. 2 is a AUC in year 1 to year 11 during follow-up after surgery in the patient cohort from Sotiriou et al. (8).

FIG. 3 is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts. FIG. 3 is a Time-dependent ROC (t=5 years) curves of the 28-gene signature on two validation sets. AUC=0.843 with 25 overlapping genes on data from van't Veer et al.(27) AUC=0.764 with 8 overlapping genes on data from Sorlie et al. (10).

FIG. 4 is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts. FIG. 4 is a AUC in year 1 to year 13 during follow-up after surgery on two independent patient cohorts (10;28).

FIG. 5 is a Time-dependent ROC analyses of the 28-gene signature in overall survival prediction in there breast cancer patient cohorts. FIG. 5 is a Time-dependent ROC curves at time=5 years. AUC=0.927 on data from Sotiriou et al.(8) AUC=0.808 on data from Sorlie et al.(10)

FIG. 6 is a Time-dependent ROC analyses of the 28-gene signature in overall survival prediction in there breast cancer patient cohorts. FIG. 6 is the area under the ROC curve (AUC) of overall survival prediction during the follow-up after surgery.

FIG. 7 is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts. FIG. 7 are Time-dependent ROC curves at time=5 years. AUC=0.92 on data from Sotiriou et al. (8)

FIG. 8 is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts. FIG. 8 are Time-dependent ROC curves at time=5 years. AUC=0.87 on data from Sorlie et al.(10)

FIG. 9 is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts. FIG. 9 are Time-dependent ROC curves at time=5 years. AUC=0.79 on data from van't Veer et al. (26).

FIG. 10 is a Time-dependent ROC analyses of 24 genes within the 28-gene signature in relapse-free survival prediction in one ovarian cancer patient cohort from Bild et al. (29)

DETAILED DESCRIPTION OF THE INVENTION

A first embodiment in this application can be an expression profile-defined prognostic model able to predict the recurrence and metastases of breast cancer and ovarian cancer by using unique gene expression patterns in tumors. Additionally, the expression profile-defined prognostic model may be used to predict the relapse-free interval and metastases-free interval. The expression based profile-defined prognostic model has been developed and is a highly accurate predictor of disease-free survival as well as overall survival in individual breast cancer patients. The expression based profile-defined prognostic model can be a gene signature such as a 15-gene signature, a 23-gene signature, or a 28-gene signature comprised of a combination of the following genes (Table 1).

TABLE 1 28 genes that quantifies disease-free survival and overall survival of breast cancer UniGene Gene Clone_IMAGE Cluster ID Sequence ID FAM134C 198917 Hs.463079 NM_178126 TOMM70A 198312 Hs.227253 NM_014820 MCF2 268412 Hs.387262 NM_001099855 NM_005369 RAD52 Pseudogene 1377154 Hs.552577 NM_134424 MCM2 239799 Hs.477481 NM_004526 C18B11 131988 Hs.173311 NM_152260 SEC13L 757210 Hs.301048 NM_031216 NM_001013437 SLC25A5 291660 Hs.522767 NM_001152 PLSCR1 268736 Hs.130759 NM_021105 TXNRD1 789376 Hs.434367 NM_003330 NM_001093771 NM_182742 NM_182729 NM_182743 RAD50 261828 Hs.242635 NM_005732 NM_133482 — 46196 BX100884 H09243 H09242 INPPL1 703964 Hs.523875 NM_001567 — 501651 Hs.439445 AK025546 PBX2 80549 Hs.509545 NM_002586 SSBP1 125183 Hs.490394 NM_003143 — 34396 Hs.448229 BE870371 PDGFRA 376499 Hs.74615 NM_006206 ACOT4 488202 Hs.49433 NM_152331 DDOST 50666 Hs.523145 NM_005216 IGHA1 182930 Hs.497723 AK128652 S100P 135221 Hs.2962 NM_005980 FAT 591266 Hs.481371 NM_005245 FGF2 324383 Hs.284244 NM_002006 INSM1 22895 Hs.89584 NM_002196 IRF5 260035 Hs.521181 NM_001098629 NM_002200 NM_001098627 NM_001098630 NM_001098628 NM_032643 NM_001098631 SMARCD2 741067 Hs.250581 NM_001098426 NM_003077 MAP2K2 769579 Hs.465627 NM_030662

There is no overlap between the disclosed gene signature and previously reported gene signatures. Of the 28 genes in Table 1, 17 are related to tumorigenesis (Table 2) and 9 genes are linked to breast cancer pathogenesis (Table 3). Furthermore, among the nine breast cancer-related genes, five genes are established breast cancer biomarkers ((MCM2, Rad50, PDGFRA, S100P, and FGF2) (Table 3)).

TABLE 2 Genes that are related to tumorigenesis Gene Gene Name Function MCF2 Mcf.2 cell line derived Guanine nucleotide exchange transforming sequence factor MCM2 Mcm2 minichromosome DNA replication maintenance deficient 2, mitotin SEC13L Seh1-like mRNA export, nuclear pore distribution and cell division PLSCR1 Phospholipid scramblase 1 Lipid transfer signaling RAD50 RAD50 homolog DNA repair INPPL1 Inositol polyphosphate Lipid metabolism phosphatase-like 1 TXNRD1 Thioredoxin reductase 1 Antioxidant and redox regulator PBX2 Pre-b-cell leukemia Transcriptional repressor and transcription factor 2 tumor suppressor SSBP1 Single-stranded dna DNA binding protein binding protein 1 PDGFRA Platelet-derived growth Growth factor receptor factor receptor S100P S100 calcium binding Cell differentiation protein p FAT Fat tumor suppressor Cell signaling suppressor homolog 1 FGF2 Fibroblast growth factor 2 Signaling tranduction INSM1 Insulinoma-associated 1 Transcriptional repressor IRF5 Interferon regulatory Tumor suppressor gene factor 5 SMARCD2 Swi/snf related, matrix chromatin remodelling associated, actin dependent regulator of chromatin, subfamily d, member 2 MAP2K2 Mitogen-activated protein Signaling transduction kinase kinase 2

TABLE 3 Genes that are linked to breast cancer pathogenesis Breast Cancer Gene Gene Name Function Involvement MCF2 Mcf.2 cell line derived Guanine nucleotide (+) transforming sequence exchange factor MCM2 Mcm2 minichromosome DNA replication (+) maintenance deficient 2, mitotin biomarker (1) RAD50 DNA repair (+) biomarker (2) TXNRD1 Thioredoxin reductase 1 Antioxidant and (+) redox regulator PDGFRA Platelet-derived growth factor Growth factor (+) receptor receptor biomarker (3; 4) S100P S100 calcium binding protein p Cell differentiation (+) biomarker (5; 6) FGF2 Fibroblast growth factor 2 Signaling (+) tranduction biomarker (7) SMARCD2 Swi/snf related, matrix associated, chromatin (+) actin dependent regulator of remodelling chromatin, subfamily d, member 2 MAP2K2 Mitogen-activated protein kinase Signaling (+) kinase 2 transduction

Based upon the expression profiles of these 28 genes in the data from Sotiriou et al. (8), a Linear Discriminant Analysis function classified 5-year relapse status for patients provided an accuracy of 0.92, a sensitivity of 1.90, and a specificity of 0.95. To evaluate relapse-free survival prediction, a Cox proportional hazards model was built on the 28-gene signature and the risk score was used to construct the time-dependent receiver operating curve (ROC). The area under the ROC curve (AUC) during year five was 0.983 (FIG. 1), and remained 0.92 between years 8 and 11 during the follow up (FIG. 2).

To evaluate the prognostic power of the identified gene signature, two independent validation sets were used (9;10). Using the signature genes, time-dependent ROC analyses were performed to evaluate relapse/metastases prediction on two independent patient cohorts (FIGS. 3 and 4). The area under the ROC (5-year) curve on the data from van't Veer et al. (11) was 0.843 with 25 signature genes in predicting metastatic potential. The AUC (5-year) was 0.764 on the data from Sorlie et al. (10) with eight overlapped genes in the relapse-free survival prediction (FIG. 3).

Time dependent ROC analysis showed that the 28-gene signature was also predictive of overall survival (P<0.001; FIGS. 5 and 6). In the prediction of overall survival , the AUC (5-year) was 0.927 on data from Sotiriou et al. (Sotiriou C. et al., Breast Cancer classification and prognosis based on gene expression profiles from a population-based study, Proc. Natl. Acad. Sci., USA 2003; 100:10393-8) and 0.808 on data from Sorlie et al.(Sorlie T. et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications, Proc. Natl. Acad. Sci., USA 2001; 98:10869-74).

Among the 28-gene signature, 11 genes had significant association with relapse-free survival in Cox modeling (Table 4).

TABLE 4 Genes that are significantly associated with breast cancer relapse. GENE P-value FGF2 0.0039 SLC25A5 0.0051 C18B11 0.0062 SMARCD2 0.0087 TOMM70A 0.0250 PBX2 0.0330 SEC13L 0.0350 Clone ID: 501651 0.0350 IRF5 0.0350 DDOST 0.0470 Clone ID: 182930 0.0520

Among the 28-gene set, 15 genes (Table 5) predict disease-free survival with an accuracy ranging from 0.79 to 0.92 in three patient cohorts from Sotiriou et al. (8), van't Veer et al. (12), and Sorlie et al. (10) (FIGS. 7, 8, and 9). These 15 genes can be used as a 15-gene signature prognostic model for breast cancer. In addition, the 8 unique genes from Table 4 may be added to form a 23-gene signature prognostic model for breast cancer. The remaining 5 unique genes from Table 1 form a 28-gene signature prognostic model for both breast and ovarian cancer. Together, genes in Tables 4 and 5 can predict both breast cancer relapse and metastases.

TABLE 5 Genes that predicts breast cancer relapse. GENE CLONE ID MAP2K2 769579 SMARCD2 741067 S100P 2060823 FAT 591266 DDOST 50666 SSBP1 125183 PDGFRA 1643186 INPPL1 703964 RAD50 261828 PLSCR1 268736 RAD52 140004 C18B11 131988 MCM2 239799 MCF2L 1781388 TXNRD1 630625

To assess a breast cancer patient's relapse and metastatic potential, risk scores can be generated by using a Cox model of the 28-gene signature, independent of clinical-pathological parameters although any standard risk evaluation could be used. In this appication large value of the risk scores indicates a high risk of relapse/metastases, while a small value indicates a lower risk of breast cancer relapse. The 28-gene signature obtained from the training set (8) was fitted into a Cox regression model as covariates. To avoid overfitting, the data set are randomly partitioned into two subsets—one was used to define risk groups by fitting the model and obtaining the risk score cutoffs; the other subset was used to validate the cutoffs for defining the risk groups. The distribution of the risk scores can be categorized into groups of two or more. If two groups, patients could be labeled as high risk at the 65^(th) percentile or above and low risk at 64^(th) percentile and below. Alternatively, the patients could be categorized into high, low, or intermediate risk group is 39%, 26%, and 35%, respectively in the training set. The cutoffs defined in the training subset can be used to separate the patients in the test subset into high, low and intermediate risk groups.

A further embodiment is the ability to evaluate clincopathogic variables for cancer patients. Clincopathogic variable includes, but is not limited to, average metastases-free days, ER and PR status, age, tumor size, and tumor grade. Table 6 displays the clinical characteristics of each risk group, including average relapse-free days, ER status, Her2/neu overexpression, nodal status, age, tumor size, and treatment received on the data from Sotiriou et al.(8).. Risk scores were generated for patients in Cox modeling using the gene expression profiles, without including clinicopathologic parameters. The 39^(th) and 65^(th) percentile of the risk scores were used to partition patients into high, intermediate, and low risk groups. Same analysis is applied to the two validation sets. Table 7 summarizes the clinical characteristics of each risk group, including average metastases-free days, ER and PR status, age, tumor size, and tumor grade on the data from van't Veer et al. (13). Table 8 summarizes the clinical characteristics of each risk group, including average relapse-free days, ER status, age, and tumor grade on the data from Sorlie et al.(10).

TABLE 6 Clinical characteristics of each risk group on the patients from Sotiriou et al. (8) # of Her- % of Average % of 2\neu % of Positive Risk RFS Age ≧ positive Tumor Nodal % of % of % of Group (days) 50 yrs cases Size > 2 cm Status Chemo Hormone ER+ High 969 82% 6 82% 67% 38% 79% 54% Inter. 2407 73% 1 58% 50% 35% 85% 58% Low 2781 65% 0 47% 41% 24% 74% 85%

TABLE 7 Clinical characteristics of each risk group on the patients from van't Veer et al. (14) Average % of % of Risk % of RFS % of % of Tumor T3/T4 Group Patients (days) Age ≧ 50 ER+ Grade 3 Tumors High 28% 553 50% 69% 81% 94% Intermediate 32% 801 84% 89% 26% 89% Low 40% 1376 70% 73% 32% 77%

TABLE 8 Clinical characteristics of each risk group (Sorlie et al. (10)) Average % of % of Risk % of RFS % of % of Tumor T3/T4 Group Patients (days) Age ≧ 50 ER+ Grade 3 Tumors High 28% 553 50% 69% 81% 94% Intermediate 32% 801 84% 89% 26% 89% Low 40% 1376 70% 73% 32% 77%

Clinical variables such as nodal status, tumor size, tumor grade, ER status and HER2/neu overexpression in breast cancer patients affect the disease outcomes. The clinical characteristics of each risk group in the studied cohorts are analyzed including average disease-free survival days, ER and PR status, HER2/neu overexpression, nodal status, age, tumor size, grade, and treatment received. The 28-gene signature is strongly associated with the clincopathogic variables, including tumor size, tumor grade, ER and PR status, and HER2/neu overexpression (P<0.05; Table 9).

TABLE 9 Association of gene expression-defined risk groups and clinicopathologic parameters P-Values van't Veer Risk Groups vs. Sotiriou et al. (8) et al. (15) Sorlie et al. (10) Age¹ 0.243 0.458 0.095 (<50 yrs or ≧50 yrs) Tumor size 0.006* 0.047* (<2 cm or >2 cm) Tumor grade 0.041* 0.004* 0.001* (½ vs. 3) ER status 0.011* 0.004* 0.296 PR status 0.001* Her2/neu 0.020* ¹The percentage of patients who were at least 50 years old was 74%, 28%, and 69% in the cohorts from Sotiriou et al. (8), van't Veer et al. (16), and Sorlie et al. (10), respectively.

The 28-gene signature also predicts tumor recurrence in ovarian cancer with an accuracy of 0.89 (FIG. 10). Table 10 listed the genes that are predictive of ovarian cancer relapse.

TABLE 10 24 genes that quantifies relapse-free survival of breast cancer UniGene Gene Clone_IMAGE Cluster ID Sequence ID FAM134C 198917 Hs.463079 NM_178126 TOMM70A 198312 Hs.227253 NM_014820 MCF2 268412 Hs.387262 NM_001099855 NM_005369 RAD52 Pseudogene 1377154 Hs.552577 NM_134424 MCM2 239799 Hs.477481 NM_004526 C18B11 131988 Hs.173311 NM_152260 SEC13L 757210 Hs.301048 NM_031216 NM_001013437 SLC25A5 291660 Hs.522767 NM_001152 PLSCR1 268736 Hs.130759 NM_021105 TXNRD1 789376 Hs.434367 NM_003330 NM_001093771 NM_182742 NM_182729 NM_182743 RAD50 261828 Hs.242635 NM_005732 NM_133482 INPPL1 703964 Hs.523875 NM_001567 PBX2 80549 Hs.509545 NM_002586 SSBP1 125183 Hs.490394 NM_003143 PDGFRA 376499 Hs.74615 NM_006206 DDOST 50666 Hs.523145 NM_005216 IGHA1 182930 Hs.497723 AK128652 S100P 135221 Hs.2962 NM_005980 FAT 591266 Hs.481371 NM_005245 FGF2 324383 Hs.284244 NM_002006 INSM1 22895 Hs.89584 NM_002196 IRF5 260035 Hs.521181 NM_001098629 NM_002200 NM_001098627 NM_001098630 NM_001098628 NM_032643 NM_001098631 MAP2K2 769579 Hs.465627 NM_030662

In the present invention, target polynucleotide molecules are extracted from a sample taken from an individual afflicted with breast cancer or ovarian cancer. The sample may be collected in any clinically acceptable manner, but must be collected such that marker-derived polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids derived there from (i.e., cDNA or amplified DNA) can be labeled distinguishably from standard or control polynucleotide molecules, and both are simultaneously or independently hybridized to a detection mechanism. A detection mechanism can be any standard comparison mechanism such as a microarray or an assay of reverse transcription polymerase chain reaction (RT-PCR) comprising some or all of the markers or marker sets or subsets described above. This process identifies positive matches. Alternatively, mRNA or nucleic acids derived therefrom may be labeled with the same label as the standard or control polynucleotide molecules to identify positive matches, wherein the intensity of hybridization of each at a particular probe or primer is compared for such an identification. A sample may comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspiration, or a sample of bodily fluid, such as blood, plasma, serum, lymph, ascetic fluid, cystic fluid, urine, or nipple exudate. The sample may be taken from a human, or from non-human animals such as horses; mice, ruminants, swine or sheep. Patients' gene expression levels may be quantified by any means known in the art based on the marker sets defined above. Patients may be classified based on the quantitative expression profiles using any means known in the art. For example, the risk scores of a patient cohort may be generated using a Cox proportional hazard model. Patients with a risk score greater than the median is defined as high risk, whereas patients with a risk score less than the median is classified as low risk. Alternatively, a patient may be classified as high risk if this patient's gene expression profile is correlated with the high risk signature, or classified as low risk if this patient's gene expression profile is correlated with the low risk signature. A patient's prognostic categorization can also be determined by using a statistical model or a machine learning algorithm, which computes the probability of recurrence based on this patient's gene expression profiles. Cutoffs can be defined for patient stratification based on specific clinical setting. In addition, patients may be defined into three risk groups in the prognostic categorization based on the marker sets defined above.

Methods for preparing total and poly(A)+RNA are well known and are described in (17). RNA may be isolated from eukaryotic cells by procedures that involve cell lysis and denaturation of the proteins contained therein. Cells of interest include wide-type cells (i.e., no mutation), drug-treated wild-type cells, tumor- or tumor-derived cells, modified cells, normal or tumor cell lines cells, and drug-treated modified cells. Total RNA may also be extracted from samples using commercially available kits such as the RNeasy mini kit according the manufacturer's protocol (Qiagen, USA).

Additional steps may be performed to remove DNA (17). If desired, RNase inhibitors may be added to the lysis buffer. Likewise, a protein denaturation/digestion step may be added to the protocol. mRNA may be purified by means such as magnetic separation using Dynabeads (Dynal) or the Invitrogen FastTrack 2.0 kit (10).

For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Total RNA may also be linearly amplified using the original or modified Eberwine method (18) and be used as a reference for cDNA analysis (8).

The sample of RNA can comprise a plurality of different mRNA molecules, each different mRNA molecular having a different nucleotide sequence. In a specific embodiment, the RNA sample has not been functionally annotated.

The present invention provides a set of biomarkers for the identification of conditions of indications associated with breast cancer. Generally, the markers sets were identified by determining which of ˜25,000 human genes had expression patterns that correlated with the conditions or indications.

In one embodiment, the expression of all markers in a sample X is compared to the expression of all markers in the 28-gene signature or subsets as described above derived from tumor samples. The comparison may be accomplished by any means known in the art. The expression level may be determined by isolating and determining the level (i.e., the abundance) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene may be determined. For example, expression levels of various markers may be measured by separation of target nucleotide molecules (e.g., RNA or cDNA) derived from the markers in agarose or polyacrylamide gels, followed by hybridization with marker-specific oligonucleotide probes. Alternatively, the comparison may be accomplished by the labeling of target polynucleotide molecules followed by separation on a sequence gel. The comparison may also be accomplished by measuring the gene expression level using real-time reverse transcription polymerase chain reaction with marker-specific primers/probes. Patients may be classified based on the quantitative expression profiles using any means known in the an For example, the risk scores of a patient cohort may be generated using a Cox proportional hazard model. Patients with a risk score greater than the median is defined as high risk, whereas patients with a risk score less than the median is classified as low risk. Alternatively, a patient may be classified as high risk if this patient's gene expression profile is correlated with the high risk signature, or classified as low risk if this patient's gene expression profile is correlated with the low risk signature. A patient's prognostic categorization can also be determined by using a statistical model or a machine learning algorithm, which computes the probability of recurrence based on this patient's gene expression profiles. Cutoffs can be defined for patient stratification based on specific clinical setting. In addition, patients may be defined into three risk groups in the prognostic categorization based on the marker sets defined above.

A marker is selected based on its predictive power of breast cancer recurrence, including local recurrence and distant metastasis. A combination of Random Forests (19) and Linear Discriminant Analysis (LDA) is used to identify gene signatures for predicting breast cancer recurrence/metastases. Random forests of software R is first used to identify a small subset of genes from the original microarray data. Linear Discriminant Analysis of software SAS is used to further refine the gene signature.

Random forests are a generalization of the standard tree algorithms (20). The basic step of random forests is to form diverse tree classifiers from a single training set. Each tree is built upon a bootstrap sample from the training set. The variables used for splitting the tree nodes are a random subset of the whole variables set. The classification decision of a new case is obtained by majority voting (unless the cutoff value is user defined) over all trees. In random forests, about one-third of the cases in the bootstrap sample are not used in growing the tree. These cases are called “out-of-bag” (OOB) cases and are used to evaluate the algorithm performance. A very important function of random forests is variable importance evaluation. The importance of a variable is defined in terms of its contribution to classification accuracy. Based on the variable importance measure, backward elimination was used to identify the gene subset with the smallest OOB error rate. Here, the OOB error rate was not used to assess the prediction accuracy of the identified gene subsets. Instead, it served as a stopping rule for feature selection. The varSelRF package of software R (21) was used according to the following steps:

1. Build a forest with N trees and obtain a ranking of variable importance

2. Remove 20% of the least important variables

3. Construct a new forest with K trees

4. Repeat steps 2 and 3 until two genes are left

5. Select the gene subset with the smallest OOB error rate

In the experiments, N=3,000 and K=1,000 are chosen because the large number of trees in the initial forests are likely to produce stable importance measures (21). The “0-Standard Error (0-SE) rule” is used, which identifies the gene subset with the smallest 00B error rate. The “0-SE rule” usually selects more genes than the “1-SE rule” does. Since further gene filtering would be performed by using Linear Discriminant Analysis, the gene subsets are selected with the lowest prediction error using random forests.

Discriminant analysis is used to determine which variables discriminate two or more naturally occurring groups in prognosis. Given a number of variables as the data representation, each class is modeled as multivariate normal distribution with a covariance matrix and a mean vector. Instances are classified to the label of the nearest mean vector based on Mahalanobis distance. The decision surfaces between classes become linear if the classes have a common covariance matrix. When the distribution within each group is assumed to be multivariate normal, a parametric method can be used to develop a discriminant function. Such function is determined by a measure of generalized square distance which is based on the pooled covariance matrix as well as the prior probabilities of group membership. The generalized squared distance D_(i) ²(x) from input x to class i is:

D _(i) ²(x)=d _(i) ²(x)+g(i)

where d_(i) ²(x)=(x−m)′V⁻¹(x−m_(i)) is the squared distance from x to group I; m_(i) is the p-dimensional mean vector for group I; V is the pooled covariance matrix and g(i) depends on the prior probability of class i. In practice, the prior probability can be assumed as equal for all groups (refer to SAS Users' Manual). In this study, we assumed equal prior probability and thus g(i)=0. x is classified into class I, if D_(i) ²(x) is the smallest among all the distance measures. We selected the gene markers using backward selection of stepwise discriminant analysis with software SAS.

Linear Discriminant Analysis (LDA) is used to refine the gene signature obtained from random forests and assess the classification accuracy of models in predicting 5-year relapse-free survival based on the identified gene signatures. Leave-one-out cross-validation is used in the evaluation to identify the optimal marker subset (22).

Once a marker set is identified, validation of the marker set may be accomplished by a survival analysis. To evaluate the accuracy of survival prediction, time-dependent receiver operating characteristic (ROC) analysis for censored data (23;24) was performed with software R. Time-dependent ROC analysis extends the concepts of sensitivity, specificity, and ROC curves for time-dependent binary disease variables in censored data. In this embodiment, the binary disease variable R_(i)(t)=1, if patient i has recurrent or metastatic breast cancer prior to time t; otherwise, R_(i)(t)=0. For a diagnostic marker M, both sensitivity and specificity are defined as a function of time t:

sensitivity(c,t)=P{M>c|R(t)=1}

sensitivity(c,t)=P{M≦c|R(t)=0}

A ROC(t) is a function of t at different cutoffs c. A time-dependent ROC curve is a plot of sensitivity(c, t) vs. 1−specificity(c, t). The area under the ROC curve (AUC) can be used as an accuracy measure of the ROC curve. A higher prediction accuracy is evidenced by a larger AUC(t) (23;24).

The prediction of patient outcome may be accomplished with any means known in the art. For example, to estimate a patient's recurrent and metastatic potential, risk scores are generated by fitting the identified gene predictors in a Cox proportional hazard model as covariates. A higher risk score represents a higher probability of tumor recurrence. The distribution of the risk scores can be used to classify the patients into three groups: high-risk, low-risk, and intermediate-risk. Alternatively, patients may be stratified into two groups: high- or low-risk. Kaplan-Meier analysis may be used to assess the disease-free survival probability of three risk groups in the studied patient cohorts (8;10;25). Similarly, a Cox proportional hazard model may be developed to estimate a patient's overall survival probability. A higher survival risk score represents a higher risk for death from breast cancer. Alternatively, a Linear Discriminant Analysis (LDA) function may be determined by a measure of generalized square distance which is based on the pooled covariance matrix based on the marker sets described above as well as the prior probabilities of group membership for prognostic categorization.

For prognostic predictions in clinic, the expression levels of the markers can be measured with any means known in the art such as cDNA microarrays (8;10;26), various generations of Affymetrix gene chips (Affymetrix, Santa Clara, Calif.), and real-time reverse transcription polymerase chain reactions. The present invention further provides for kits comprising the marker sets above. The analytical methods described above can be implemented by use of following computer systems. For example, a computer system can be an Intel 8086-, 80386-, 80486-, or Pentium-based process with preferably 64 MB or more of main memory. The computer system can be linked to an external component, including mass storage. This mass storage can be one or more hard disks, preferably of 1 GB or more storage capacity. Other external components include regular accessories for a computer such as a monitor, a mouse, or a printer.

The software program described in above sections can be implemented with software packages R and SAS. The software to be included in the kit comprises the data analysis methods for this invention as disclosed herein. In particular, the software algorithms may include mathematical procedures for biomarker discovery, including the computation of the Mahalanobis distance between clinical categories (i.e., relapse status) and marker expression. The software may also include mathematical procedures for computing the regression coefficients between the marker expression and patient survival.

Alternative computer systems and software for implementing the analytical methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims.

These terms and specifications, including the examples, serve to describe the invention by example and not to limit the invention. It is expected that others will perceive differences, which, while differing from the forgoing, do not depart from the scope of the invention herein described and claimed. In particular, any of the function elements described herein may be replaced by any other known element having an equivalent function.

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1. A method to determine metastatic potential, relapse potential, or both in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and the markers in Table 5, and developing a quantitative expression profile.
 2. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 1 further comprising the addition of unique markers in Table 4 for said search of positive matches.
 3. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 2 further comprising the addition of unique markers in Table 1 for said search of positive matches.
 4. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 3 further comprising evaluating said quantitative expression profile using risk analysis.
 5. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 4 wherein said risk analysis is a statistical model or machine learning algorithm.
 6. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 4 further, comprising placing an individual in two or more categories.
 7. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 6 wherein said categories are high risk or lower risk based on said statistical model or machine learning algorithm.
 8. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 6 wherein said categories are high risk, intermediate risk, or lower risk based on said statistical model or machine learning algorithm.
 9. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 4 wherein said risk analysis is a Cox proportional hazard model.
 10. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 14 wherein said risk analysis is a Kaplan Meier analysis for disease free survival.
 11. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 4 wherein said risk analysis is a Linear Discriminate Analysis.
 12. The method to determine metastatic potential, relapse potential, or both in breast cancer patients of claim 4 further comprising assessing clincopathogic variables.
 13. A method to determine relapse free potential in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and the markers in Table 10, and developing a quantitative expression profile.
 14. The method to determine relapse free potential in breast cancer patients of claim 13 further comprising evaluating said quantitative expression profile using risk analysis.
 15. The method to determine relapse free potential in breast cancer patients of claim 14 wherein said risk analysis is a statistical model or machine learning algorithm. 